Methods, devices, and computer program products for standardizing a fermentation process

ABSTRACT

Methods of standardizing a fermentation process may include obtaining a fluidic sample, measuring one or more physical parameters of the sample, comparing the measurement of the physical parameter of the material to a baseline value of the physical parameter for the fermentation process, and responsive to a deviation of the measurement of the physical parameter from the baseline value, determining a remediation action based on a correlation between the physical parameter and regulatory genes of a fermentation organism.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation Application of U.S.Application No. 16/142,736 filed on Sep. 26, 2018, which is a U.S.Nonprovisional Pat. Application of, and claims priority under 35 U.S.C.§119(e) to, U.S. Provisional Pat. Application Serial Number 62/564,816,filed Sep. 28, 2017, and entitled, “METHODS, DEVICES, AND COMPUTERPROGRAM PRODUCTS FOR YEAST PERFORMANCE MONITORING IN FERMENTATIONSYSTEMS,” all of the above mentioned applications are incorporated byreference in their entireties.

This application is also related to U.S. Application No. 18/194,282,filed Mar. 31, 2023, U.S. Application No. 17/652,402 filed Feb. 24,2022, and U.S. Application No. 63/153,177 filed on Feb. 24, 2021. All ofthe above mentioned applications are incorporated by reference in theirentireties.

FIELD

Various embodiments described herein relate to methods, devices, andcomputer program products for fermentations systems and, moreparticularly, to fermentations systems that incorporate yeastmonitoring.

BACKGROUND

Fermentation of grain extracts for the purpose of ethanol production bySaccharomyces cerevisiae and other microorganisms is an ancientinvention. In spite of much progress, control of this process is stillvery primitive. Modern fermentation facilities are equipped to: receivefeedstocks of grain, fruit, and other organic material; extract solublefermentable sugars using heat, enzymes, and mechanical action; perhapsadding preservative or flavoring plants such as hops; and move thisfermentable substrate to fermenters where yeast is added. Various yeastspecies (and subspecies, or strains) are able to convert much of thepresent sugars to carbon dioxide and ethanol, yieldingethanol-containing beverages, especially beer. Successful fermentationsend when the beer reaches the desired alcohol content and has otherflavor and color characteristics consistent with a particular beerstyle. However, once a fermentation is initiated by mixing yeast andwort in a fermentation vessel, the brewer has almost as little controlover the process as his ancient predecessors who may have simplyexclaimed, Alea iacta est. Other fermentation processes, such as thoseused to produce other food and beverage products or fine chemicals orpharmaceuticals operate in the same way. They are begun and end when theyield targets are satisfied.

One assumption of fermentation in this way is that providing highlysimilar initial ingredients and conditions will yield a highly similarproduct. While most professional brewers succeed in routinely producingproducts that meet high quality thresholds, the costs of doing so areunnecessarily high because the time required for the fermentations andcertain output characteristics often vary in ways that cannot beanticipated at the outset and may not be known for several days or evenprior to the end of the fermentation.

SUMMARY

Various embodiments described herein provide methods, devices, andcomputer program products for the monitoring and remediation of yeastperformance in fermentations systems.

According to some embodiments described herein, a fermentationmonitoring system for a fermentation process using a fermentationorganism includes a fluidic sampling apparatus configured to be coupledto a fermentation tank and sample material from the fermentation tank, aphysical sensor array coupled to the fluidic sampling apparatusconfigured to provide measurements of at least one physical parameter ofthe material sampled from the fermentation tank, an analytic systemcomprising at least one processor communicatively coupled to thephysical sensor array and configured to receive the measurements of thephysical sensor array, and a memory coupled to the at least oneprocessor and including computer readable program code. When executed bythe at least one processor, the computer readable program code causesthe at least one processor to perform operations including receiving themeasurement of the at least one physical parameter of the materialsampled from the fermentation tank, comparing the measurement of the atleast one physical parameter of the material sampled from thefermentation tank to a baseline value of the at least one physicalparameter for the fermentation process, and, responsive to a deviationof the measurement of the at least one physical parameter of thematerial sampled from the fermentation tank from the baseline value,determining a remediation action based on a correlation between the atleast one physical parameter and one or more regulatory genes of thefermentation organism.

According to some embodiments described herein, a method for monitoringa fermentation process includes collecting a sample of materialcomprising a fermentation organism that is in a fermentation tankperforming the fermentation process, said collecting performed byutilizing a fluidic sampling apparatus coupled to the fermentation tank,utilizing a physical sensor array coupled to the fluidic samplingapparatus to measure at least one physical parameter of the sample,comparing the measurement of the at least one physical parameter of thesample to a baseline value of the at least one physical parameter forthe fermentation process, responsive to a deviation of the measurementof the at least one physical parameter of the sample from the baselinevalue, determining a remediation action based on a correlation betweenthe at least one physical parameter of the sample and one or moreregulatory genes of the fermentation organism, and performing theremediation action to alter a state of the material in the fermentationtank.

According to some embodiments described herein, a fermentationmonitoring system for a fermentation process using a fermentationorganism includes a fluidic sampling apparatus configured to be coupledto a fermentation tank and sample material from the fermentation tank, aphysical sensor array coupled to the fluidic sampling apparatusconfigured to provide measurements of at least one physical parameter ofthe material sampled from the fermentation tank, an analytic systemcomprising at least one processor communicatively coupled to thephysical sensor array and configured to receive the measurements of thephysical sensor array, a storage medium coupled to the analytic system,and a memory coupled to the at least one processor and includingcomputer readable program code. The computer readable program code, whenexecuted by the at least one processor, causes the at least oneprocessor to perform operations including receiving a plurality ofmeasurements of the at least one physical parameter of the materialsampled from the fermentation tank at multiple time points during thefermentation process from initiation to termination of the fermentationprocess, thereby providing values (or a rate of change) for the at leastone physical parameter over time for the fermentation process, measuringa transcriptome of the fermentation organism at the time points duringthe fermentation process as measured for the at least one physicalparameter to produce a gene expression database over time for thefermentation process, inferring regulatory networks of the fermentationorganism from the gene expression database, identifying one or moreregulatory genes of the fermentation organism that are correlated with avalue or range of values (or a rate of change) for the at least onephysical parameter measured for the fermentation process, therebyconstructing a baseline database for the fermentation process thatprovides a predetermined value or range of values (or predetermined rateof change) for the at least one physical parameter that is correlatedwith the one or more regulatory genes of the fermentation organism, andstoring the baseline database in the storage medium.

According to some embodiments described herein, a method forconstructing a baseline database for a selected fermentation process bya fermentation organism in a fermentation substrate, includes (a)measuring a physical parameter at multiple time points during theselected fermentation process from initiation to termination of theselected fermentation process, thereby providing values (or a rate ofchange) for the physical parameter over time for the selectedfermentation process, (b) measuring a transcriptome of the fermentationorganism at the same time points during the fermentation process asmeasured for the physical parameter to produce a gene expressiondatabase over time for the selected fermentation process, (c) inferringregulatory networks of the fermentation organism from the geneexpression database, and (d) identifying one or more regulatory genes ofthe fermentation organism that are correlated with a value or range ofvalues (or a rate of change) for the physical parameter measured for theselected fermentation process, thereby constructing the baselinedatabase for the selected fermentation process that provides apredetermined value or range of values (or predetermined rate of change)for the parameter that is correlated with the one or more regulatorygenes of the fermentation organism.

According to some embodiments described herein, a method ofstandardizing a selected fermentation process by a fermentation organismin a fermentation substrate, includes (a) measuring a physical parameterat multiple time points during the selected fermentation process frominitiation to termination of the selected fermentation process, therebyproviding values (or a rate of change) for the physical parameter overtime for the selected fermentation process, (b) comparing values (or arate of change) of the physical parameter measured for the selectedfermentation with predetermined values (or predetermined rate of change)for the same physical parameter provided by a baseline database, (c)modifying a fermentation condition to increase or decrease theexpression of one or more regulatory genes of the fermentation organismidentified in the baseline database as correlated with the physicalparameter when the values (or the rate of change) of the physicalparameter measured for the selected fermentation process fall outsidethe predetermined range of values (or the predetermined rate of change)for the same physical parameter, thereby modifying/adjusting the values(or the rate of change) for the physical parameter so that they fallwithin the predetermined range of values (or the predetermined rate ofchange) of the baseline database and standardizing the selectedfermentation process.

In accordance with one or more preferred embodiments described herein, amethod provides a technical solution to the technical problem ofstandardizing a selected fermentation process by a fermentation organismin a fermentation substrate. The method includes first, constructing abaseline database for the selected fermentation process by thefermentation organism in the fermentation substrate by initiating afirst instance of the selected fermentation process by the fermentationorganism in the fermentation substrate and obtaining, at each respectivetime point of a plurality of predefined time points defined from thebeginning of the initiated first instance of the fermentation process, arespective fluidic sample, measuring, using each respective fluidicsample for the first instance, one or more physical parameters for therespective fluidic sample at the corresponding respective time point,determining one or more physical parameter values for the first instancebased on the measuring for the first instance, the one or more physicalparameter values including values at a point in time and valuesrepresenting a rate of change, and measuring, using each respectivefluidic sample for the first instance, a transcriptome of thefermentation organism at the corresponding respective time point. Suchmeasuring includes isolating RNA from the fermentation substrate of therespective fluidic sample, purifying the RNA isolated from thefermentation substrate of the respective fluidic sample, and measuringthe RNA. The constructing of a baseline database further includesdetermining gene expression data for the selected fermentation processbased on the obtained measurements by filtering determined physicalparameter and gene expression data to generate a first dataset whichonly includes dynamic physical parameter values and dynamic geneexpression values, computationally normalizing dynamic physicalparameter values and dynamic gene expression values of the first datasetto generate a normalized dataset, determining one or more possibleregulators by identifying dynamic gene expression values of thenormalized dataset that correspond to transcription factors, andcomparing normalized dynamic physical parameter values and normalizeddynamic gene expression values of the normalized dataset as targets toeach determined possible regulator. This is accomplished via amethodology which includes generating a regulation function for eachpossible regulator-target relationship, each regulation functiondefining a relationship between one of the determined possibleregulators and a downstream gene target corresponding to one of thenormalized dynamic gene expression values or a chemical change targetcorresponding to one of the normalized dynamic physical parametervalues, calculating, for each regulator-target relationship, a scorerepresenting a fit of the corresponding possible regulator to thecorresponding target, ranking each regulation-target relationship basedon the calculated scores, and assigning a confidence value to eachregulator-target relationship, determining a confidence threshold basedat least in part on data density, and constructing a regulatory networkbased on the ranked regulator-target relationships and the confidencethreshold. The constructing of a baseline database further includesconstructing, based on the ranked regulator-target relationships and theconstructed regulatory network, the baseline database for the selectedfermentation process that specifies one or more condition sets eachcomprising a preferred value or range of values, at one or morerespective time points of the plurality of predefined time points, forone or more physical parameters that have been determined based on theranked regulator-target relationships and the constructed regulatorynetwork to correspond to one or more regulatory genes of thefermentation organism, for each physical parameter forming part of acondition set, for each of the one or more respective time points of theplurality of predefined time points, an indication of one or moreregulatory genes determined based on the ranked regulator-targetrelationships and the constructed regulatory network to have arelationship to that physical parameter, and for each regulatory geneindicated to have a relationship with at least one physical parameterforming part of a condition set, for each of the one or more respectivetime points of the plurality of predefined time points, an indication ofone or more remediation actions to increase or decrease the expressionof that regulatory gene. The method further includes effecting, in afermentation vessel, a standardized instance of the selectedfermentation process by the fermentation organism in the fermentationsubstrate by initiating a second instance of the selected fermentationprocess by the fermentation organism in the fermentation substrate, andautomatically, at each respective time point of the plurality ofpredefined time points defined from the beginning of the initiated firstinstance of the fermentation process, obtaining a respective fluidicsample, measuring, using the respective fluidic sample for the secondinstance, one or more physical parameters for the respective fluidicsample at the corresponding respective time point, determining one ormore physical parameter values for the second instance based on themeasuring for the second instance, the one or more physical parametervalues including values at a point in time and values representing arate of change, and comparing determined physical parameter values forthe second instance to preferred values and ranges of values specifiedin condition sets of the baseline database. Effecting the standardizedinstance further includes automatically identifying, as a result ofcomparing at a certain one of the time points determined physicalparameter values for the second instance to preferred values and rangesof values specified in condition sets of the baseline database, a firstphysical parameter value for a first physical parameter which fallsoutside of a preferred range of values specified for the first physicalparameter by a first condition set of the baseline database,automatically determining, via lookup in the baseline database, a firstregulatory gene determined based on the ranked regulator-targetrelationships and the constructed regulatory network to have arelationship to the first physical parameter, automatically determining,via lookup in the baseline database, a first remediation action whichwill affect the expression of the determined first regulatory gene, thefirst remediation action comprising modifying a specified firstfermentation condition, and effecting modification of the specifiedfirst fermentation condition to affect the expression of the determinedfirst regulatory gene.

In accordance with one or more preferred embodiments with respect to thejust discussed method, effecting modification may comprise automaticallyeffecting modification.

In accordance with one or more preferred embodiments with respect to thejust discussed method, this method additionally comprises, prior toeffecting modification of the specified first fermentation condition toaffect the expression of the determined first regulatory gene,displaying, to a user via an electronic display associated with thefermentation vessel, an indication of the first physical parameter, anindication of the first physical parameter value for the first physicalparameter, an indication of the preferred range of values for the firstphysical parameter from the first condition set, an indication of thefirst regulatory gene determined based on the ranked regulator-targetrelationships and the constructed regulatory network to have arelationship to that physical parameter, and an indication of the firstremediation action which will affect the expression of the determinedfirst regulatory gene, the indication including an indication to modifythe specified first fermentation condition.

In accordance with one or more preferred embodiments with respect to thejust discussed method, this method comprises, rather than effectingmodification of the specified first fermentation condition to affect theexpression of the determined first regulatory gene, displaying, to auser via an electronic display associated with the fermentation vessel,an indication of the first physical parameter, an indication of thefirst physical parameter value for the first physical parameter, anindication of the preferred range of values for the first physicalparameter from the first condition set, an indication of the firstregulatory gene determined based on the ranked regulator-targetrelationships and the constructed regulatory network to have arelationship to that physical parameter, and an indication of the firstremediation action which will affect the expression of the determinedfirst regulatory gene, the indication including an indication to modifythe specified first fermentation condition.

In accordance with one or more preferred embodiments with respect to thejust discussed method, this method comprises, rather than constructingthe specified baseline database, constructing a baseline database thatspecifies one or more condition sets each comprising a preferred valueor range of values, at one or more respective time points of theplurality of predefined time points, for one or more physical parametersthat have been determined based on the ranked regulator-targetrelationships and the constructed regulatory network to correspond toone or more regulatory genes of the fermentation organism, and, for eachcondition set, one or more remediation actions determined, based on theranked regulator-target relationships and the constructed regulatorynetwork, to increase or decrease the expression of one or moreregulatory genes of the fermentation organism determined based on theranked regulator-target relationships and the constructed regulatorynetwork to correspond to the respective physical parameter.

In accordance with one or more preferred embodiments described herein, amethod provides a technical solution to the technical problem ofstandardizing a selected fermentation process by a fermentation organismin a fermentation substrate. The method includes maintaining a baselinedatabase for the selected fermentation process by the fermentationorganism in the fermentation substrate including data based on rankedregulator-target relationships and a constructed regulatory network forthe fermentation organism. The baseline database specifies one or morecondition sets each comprising a preferred value or range of values, atone or more respective time points of a plurality of predefined timepoints, for one or more physical parameters that have been determinedbased on the ranked regulator-target relationships and the constructedregulatory network to correspond to one or more regulatory genes of thefermentation organism. The baseline database further specifies, for eachphysical parameter forming part of a condition set, for each of the oneor more respective time points of the plurality of predefined timepoints, an indication of one or more regulatory genes determined basedon the ranked regulator-target relationships and the constructedregulatory network to have a relationship to that physical parameter.The baseline database further specifies, for each regulatory geneindicated to have a relationship with at least one physical parameterforming part of a condition set, for each of the one or more respectivetime points of the plurality of predefined time points, an indication ofone or more remediation actions to increase or decrease the expressionof that regulatory gene.

It is noted that aspects of the inventive concepts described withrespect to one embodiment, may be incorporated in a different embodimentalthough not specifically described relative thereto. That is, allembodiments and/or features of any embodiment can be combined in any wayand/or combination. Other operations according to any of the embodimentsdescribed herein may also be performed. These and other aspects of theinventive concepts are described in detail in the specification setforth below.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and features will become apparent from thefollowing description with reference to the following figures, whereinlike reference numerals refer to like parts throughout the variousfigures unless otherwise specified.

FIG. 1 illustrates a schematic representation of a monitoring systemconfigured to monitor performance of a fermentation process, accordingto various embodiments as described herein. The device is composed offour parts.

FIG. 2 illustrates a schematic representation of an example of thefluidic sampling apparatus configured to be coupled to a fermentationcontainer, according to various embodiments as described herein.

FIG. 3 illustrates a schematic representation of an example of thephysical sensor array incorporated into the fluidic sampling apparatus,according to various embodiments as described herein.

FIG. 4 illustrates the physical sensor array and the fluidic samplingapparatus coupled to a fermentation container, according to variousembodiments described herein.

FIGS. 5A and 5B illustrate example embodiments of the analytic system,according to various embodiments described herein.

FIG. 6 is a block diagram of an analytic system capable of implementingthe methods and operations associated with monitoring a fermentationprocess, according to various embodiments described herein.

FIG. 7 illustrates monitoring the performance of a fermentation organismin fermentation systems, according to various embodiments as describedherein.

FIG. 8 illustrates a method for forming a database of regulatorynetworks for a fermentation organism, according to various embodimentsas described herein.

FIG. 9 illustrates fermentation and performance control, according tovarious embodiments as described herein.

FIG. 10 illustrates an example of the process illustrated in FIG. 9 .

FIG. 11 illustrates an experimental implementation of a fermentationmonitoring system for a yeast fermentation. Multiple parameters asmeasured by real-time sensors of a fermentation performance duringproduction of beer are shown. In this example, separate sensors in thesame apparatus are measuring the temperature, pH and dissolve oxygenconcentration during the first 40 hours of fermentation.

FIG. 12 illustrates another experimental implementation of afermentation monitoring system in a yeast fermentation. pH levels asmonitored using real-time sensors from two different fermentations usingthe same recipe brewed on different days are shown. The data is shownfrom initiation of fermentation through 16 hours post-initiation.

FIG. 13 is a heat map visualization of gene expression in S. cerevisiaeduring a beer fermentation from a transcriptomics analysis according tovarious embodiments as described herein.

FIG. 14 is a selection of line plots of gene expression in S. cerevisiaeduring a beer fermentation from a transcriptomics analysis according tovarious embodiments as described herein.

DETAILED DESCRIPTION

Various embodiments will be described more fully hereinafter withreference to the accompanying drawings. Other embodiments may take manydifferent forms and should not be construed as limited to theembodiments set forth herein. Like numbers refer to like elementsthroughout.

Methods, devices, and computer program products, as described herein,provide improved instrumentation for the monitoring of fermentationsprocesses to track conformance to a baseline, and are able to improvedeviations from the baseline through remediation based on techniquesthat match specific parameters of a fermentation product to regulatorygenes of the underlying fermentation organism.

As previously noted, modern brewing processes utilizing, for example,yeast, can suffer from instances of variability, where the same orsimilar inputs yield differing results. In many cases, this variabilityis due to the differences in viability and vitality of the yeastpopulation tasked with fermentation of wort/feedstock. This variabilityis not visible or readily measurable but has a direct and oftenimmediate effect on the speed and activity of the fermentation.Therefore, there is a need to use real time monitoring of yeast healthand performance parameters to establish baselines and guidelines forvarious types and kinds of fermentations, to determine whether a givenfermentation process is proceeding according to pre-established normsand baselines, and to guide interventions to improve the timing of afermentation or characteristics of the final product.

The variable nature of fermentation processes is especially apparent forbeer breweries, as the product is not distilled or highly modified afterprimary fermentation. Therefore, beer production is used as an examplefor this description. However, it will be understood that the techniquesand devices described herein may be equally applied to otherfermentation methods without deviating from the various embodimentsdescribed herein.

As used herein, “fermentation” refers to a chemical transformation of asubstance by a microorganism. Microorganisms for fermentation mayinclude, but are not limited to, fungi (e.g., yeast), bacteria, and/oralgae (e.g., microalgae).

Once a brewery develops a recipe for a particular beer, the amounts ofthe major ingredients such as grain (which may be malted barley, wheat,corn, rice, etc.), hops, yeast, and/or water are made standard. Inaddition, certain other ingredients may be added for the benefit ofyeast health and metabolism and to aid in fermentation. Examples ofthese additives are vitamins, refined carbohydrates, minerals, pHbuffers, and dissolved gases. Complicating matters further is thepractice of re-pitching yeast, which amounts to reusing yeast from acompleted fermentation to initiate the next fermentation.

Yeast tasked with fermentation may be placed in stressful environmentalconditions and their biology and health may change each time they arere-pitched. Therefore, the nutritional and additive requirements of theyeast can change over the course of several re-pitchings. Thisvariability is difficult for brewers to gauge without the use ofsophisticated laboratory equipment and assays. As a result, levels ofthese additives are often set in ways that do not reflect the changinghealth or nutritional needs of the yeast population after a certainnumber of re-pitchings. The wide range of yeast strains and beer recipesin use, as well as the constant development of new strains and beerrecipes further complicate all of this.

This problem is generalizable to production fermentation of distillableethanol and other biologics. A variety of fermentable feedstocks, yeaststrains, and conditions are used even if the desired fermentationproduct (such as fuel ethanol, biochemicals, and/or other finechemicals) is the same. For example, though yeast is discussed, the sameor similar problems occur with other fermentation organisms such as, forexample, fungi (e.g., yeast), bacteria, and/or algae (e.g., microalgae).

It is difficult, if not impossible, to assign a specific nutritionalrequirement profile that would be useful to all breweries orfermentation facilities for all types of fermentation processes.Instead, a better tool for breweries and fermentation facilities wouldbe the ability to monitor the yeast performance over all stages of afermentation to determine their own route of intervention depending onthe age of the yeast population (pitch number) and real measurements ofchemical and/or other biologically-relevant values as they occur overtime during fermentation due to the activity of the yeast. Themeasurements can be recorded and uploaded to a user-accessible databasefor analysis and record keeping. In such a system, the monitoring may becontinuous, such that it does not interfere with the fermentationprocess. The monitoring may also be coupled to analytical tools, whichpermit inference of the state and activity of the yeast from the datacollected in near real time so that the brewer can act upon it.

FIG. 1 illustrates schematic representation of a monitoring system 100configured to monitor performance of a fermentation process, accordingto various embodiments as described herein. The monitoring system 100may contain a mechanical and fluid apparatus 220, also described hereinas a fluidic sampling apparatus 220, coupled to a fermentation container210. The fluidic sampling apparatus 220 may be further coupled to sensorarray 225, and may be communicatively coupled to analytic system 230.

The fermentation container 210 may be a container configured to containa fermentation process. In some embodiments, the fermentation container210 may be a fermentation tank for brewing beer. Fermentation tanks 210may vary in size from a few gallons to thousands of gallons. Thoughfermentation tanks 210 are used as an example herein, it will beunderstood that any container capable of containing a fermentationprocess may be used without deviating from the various embodimentsdescribed herein.

The fluidic sampling apparatus 220 may be configured to be installed onthe fermentation container 210 and to take material out of thefermentation container 210 and return it continuously. As used herein,“continuously” means that material may be taken out of the fermentationcontainer 210 and returned to the fermentation container 210 at leastonce every five minutes during the fermentation process. The materialmay be brought in by inlet connection 215. After being sampled withinthe fluidic sampling apparatus 220, the material may be returned to thefermentation container 210 via outlet connection 218. In someembodiments, the material may not be returned to the fermentationcontainer 210 (e.g., may be discarded to a drain or waste vessel).

In some embodiments, the inlet connection 215 may include multiplephysical connections between the fluidic sampling apparatus 220 and thefermentation container 210. Similarly, in some embodiments, the outletconnection 218 may include multiple physical connections between thefluidic sampling apparatus 220 and the fermentation container 210. Insome embodiments, the inlet connection 215 and the outlet connection 218may be the same physical connection to the fermentation container 210.

The fluidic sampling apparatus 220 portion of the monitoring system 100will vary with the size and type of the fermentation container 210, thepressure, flow rate, and other special requirements of the facility inwhich the fermentation container 210 is located. For example, inbreweries there may be a requirement for the fluidic sampling apparatus220 to be cleanable in place (‘CIP’) using standard, food gradechemicals and procedures. FIG. 2 illustrates a schematic representationof an example of the fluidic sampling apparatus 220 configured to becoupled to a fermentation container 210, according to variousembodiments as described herein. The inlet 215 and outlet 218 tubes mayboth be fed through the same clamped fitting 217 so that material can bepumped out of the fermentation vessel 210, passed over a physical sensorarray 225 in a sequential path and then returned to the fermentationvessel 210.

As illustrated in FIGS. 1 and 2 , the fluidic sampling apparatus 220 mayalso be further coupled to physical sensor array 225. The physicalsensor array 225 may contain one or more sensors that are configured tosample and detect physical parameters, such as chemical, biologicaland/or other parameters in the liquid from the fermentation container210 that the sensors of the physical sensor array 225 are in contactwith. The sensors included in the physical sensor array 225 may includeany sensor capable of detecting physical parameters, including chemical,biological, and /or other parameters in the fermentation product, or theenvironment surrounding the fermentation product, associated with thefermentation container 210. The physical sensor array 225 may also varywith the type of product or process. For example, particular brewingprocesses may require additional monitoring that may require additionalsensors be placed in the physical sensor array 225. The physical sensorarray 225 may be capable of sampling the physical parameters at leastonce every 15 seconds. In some embodiments, the physical sensor array225 may be capable of sampling the physical parameters more frequentlyor less frequently than 15 seconds. For example, in some embodiments,the physical sensor array 225 may be capable of sampling the physicalparameters at least once every five minutes.

FIG. 3 illustrates a schematic representation of an example of thephysical sensor array 225 incorporated into the fluidic samplingapparatus 220, according to various embodiments as described herein.Referring to FIG. 3 , the inlet 215 and outlet 218 tubes are fed througha clamped fitting 217 on the fermentation vessel 210. The fermentationsubstrate is passed through the fluidic sampling apparatus 220 by theaction of a pump mechanism 227 within the fluidic sampling apparatus220. The fermentation substrate is passed over the sensors of thephysical sensor array 225 sequentially, and then returned to thefermentation vessel 210. The physical sensor array 225 may includesensors measuring physical parameters such as chemical, biologicaland/or other parameters in the liquid fermentation substrate and maycontain additional sensors measuring physical parameters of the gasesemitted by the fermentation process. The sensors of the physical sensorarray 225 may be configured to monitor the ability of yeast to changethe environment of the fermentation container 210 and analyze theseenvironmental changes for the purposes of measuring and assessing theinternal state and fermentation performance of the yeast population inthe fermentation container 210. The liquid sensors are placed in amechanical/fluidics unit that is fitted onto a standard port (e.g.,inlet connection 215) of the fermentation container 210. The physicalsensor array 225 may be attached only to one port and may besufficiently lightweight to require no other support. Though yeast isused as an example fermentation organism, it will be understood thatphysical parameters of the fermentation process associated with otherfermentation organisms is possible without deviating from theembodiments described herein. For example, other fermentation organismsthat may be monitored include bacteria, algae, and/or other fungi,though the embodiments described herein are not limited thereto.

The fluidic sampling apparatus 220 may have a fluidics system thatsamples liquid across or through the physical sensor array 225 using apump. The liquid may then be returned to the fermentation container 210through the outlet connection 218 through a length of tubing to returnthe liquid to a different physical location within the fermentationcontainer 210. In some embodiments, the pump of the fluidic samplingapparatus 220 may be continuously operated during a fermentation.

The physical sensor array 225 may measure physical parameters of theliquid inside the fermentation container 210, including but not limitedto chemical properties, temperature, pH, dissolved oxygen content,ethanol level, CO₂ level, liquid density, gravity, cell concentration,and/or electrical conductivity, though the embodiments described hereinare not limited thereto. A portion of the physical sensor array 225 maybe placed on an off-gassing arm of the fermentation container 210 andmay measure gas flow volume and the levels of specific gases, includingbut not limited to carbon dioxide contained in the off-gas produced bythe fermentation process. The various portions of the physical sensorarray 225 may be connected by either a wireless or wired connectiondepending on model. In some embodiments, the sensors of the physicalsensor array 225 may be connected by a common circuit board that allowsfor coordination of sampling, time stamping of each data point, datastorage, and upload of sensor data by wireless transmission to off-siteservers. FIG. 4 illustrates the physical sensor array 225 and thefluidic sampling apparatus 220 coupled to a fermentation container 210,according to various embodiments described herein.

The monitoring system 100 may also include an analytic system 230. Theanalytic system 230 may be communicatively coupled to the physicalsensor array 225 and the fluidic sampling apparatus 220 to control thesensors of the physical sensor array 225, read the output of thephysical sensor array 225, and analyze the output to determine ifremediation is necessary for the fermentation process occurring in thefermentation container 210.

The analytic system 230 may be communicatively coupled to the fluidicsampling apparatus 220 via communication path 235. The communicationpath 235 may be implemented via various different technologies tocommunicate between the analytic system 230 and the fluidic samplingapparatus 220. For example, the communication path 235 may beimplemented using Radio Frequency Identification (RFID), Bluetooth, WiFi(e.g., IEEE 802.11 and variants thereof), ultrasonic transmission,optical transmission and/or various forms of radio, though theembodiments described herein are not limited thereto. In someembodiments, the communication path 235 may be a wired connection suchas, for example, Ethernet, Universal Serial Bus (USB), RS-232, RS-485,Serial Peripheral Interface (SPI), and/or Inter-Integrated Circuit(I2C), though the embodiments described herein are not limited thereto.It will be understood that the communication path between the analyticsystem 230 and the fluidic sampling apparatus 220, the analytic system230 may communicate additionally or alternatively, with the physicalsensor array 225.

FIGS. 5A and 5B illustrate example embodiments of the analytic system230, according to various embodiments described herein.

FIG. 5A illustrates an embodiment of the analytic system 230 in whichportions of the analytic system 230 are within the fluidic samplingapparatus 220. A portion of the analytic system 230 within the fluidicsampling apparatus 220 may include, in part, a micro-processorcontroller 510. The micro-processor controller 510 may be, or mayinclude, one or more programmable general purpose or special-purposemicroprocessors, digital signal processors (DSPs), programmablecontrollers, application specific integrated circuits (ASICs),programmable logic devices (PLDs), field-programmable gate arrays(FPGAs), trusted platform modules (TPMs), or a combination of such orsimilar devices. The micro-processor controller 510 may be configured toexecute computer program instructions to perform some or all of theoperations and methods for one or more of the embodiments disclosedherein.

The micro-processor controller 510 may be coupled to local storage 520.The micro-processor controller 510 may store data received from thephysical sensor array 225 in local storage 520, and may then output thedata to external analysis device 530 over communication path 235.

External analysis device 530 may process the data provided from thephysical sensor array 225 to further analyze the fermentation process ofthe fermentation container. The processing of the data may be performed,in part, by analytic module 540 executing on external analysis device530. Analytic module 540 may be executable code capable of beingexecuted on a processor of the analysis device, and configured toperform operations as described further herein. The external analysisdevice 530 may include external storage 550. The external storage 550may store data received from the physical sensor array 225 and/orresults from the analysis performed by the analytic system 230. In someembodiments, external analysis device 530 may be a cloud based centralprocessing and storage server.

FIG. 5B illustrates an embodiment of the analytic system 230 in whichall or most of the analytic system 230 is contained within the fluidicsampling apparatus 220. The embodiment of FIG. 5B may operate the sameor similar as that described with respect to FIG. 5A. However, in theembodiment of FIG. 5B, the operations performed by the external analysisdevice 530 of FIG. 5A, may be performed by the micro-processorcontroller 510. Similarly, in the embodiment of FIG. 5B, the analyticmodule 540 may execute its operations on the micro-processor controller510.

Though FIGS. 5A and 5B illustrate specific implementations of theembodiments described herein, it will be understood that these are onlyexamples, and other physical implementations of the analytic system 230are possible without deviating from the scope of the various embodimentsherein. For example, FIG. 6 illustrates an example electronic devicethat can be utilized for the analytic system 230 of the embodiments asdescribed herein.

FIG. 6 is a block diagram of an analytic system 230 capable ofimplementing the methods and operations associated with monitoring afermentation process, according to various embodiments described herein.The analytic system 230 may use hardware, software implemented withhardware, firmware, tangible computer-readable storage media havinginstructions stored thereon and/or a combination thereof, and may beimplemented in one or more computer systems or other processing systems.The analytic system 230 may also utilize a virtual instance of acomputer. As such, the devices and methods described herein may beembodied in any combination of hardware and software. In someembodiments, the analytic system 230 may be part of an imaging system.In some embodiments, the analytic system 230 may be in communicationwith the physical sensor array 225 illustrated in FIG. 1 .

As shown in FIG. 6 , the analytic system 230 may include one or moreprocessors 610 and memory 620 coupled to an interconnect 630. Theinterconnect 630 may be an abstraction that represents any one or moreseparate physical buses, point to point connections, or both connectedby appropriate bridges, adapters, or controllers. The interconnect 630,therefore, may include, for example, a system bus, a PeripheralComponent Interconnect (PCI) bus or PCI-Express bus, a HyperTransport orindustry standard architecture (ISA) bus, a small computer systeminterface (SCSI) bus, a universal serial bus (USB), IIC (I2C) bus, or anInstitute of Electrical and Electronics Engineers (IEEE) standard 1394bus, also called “Firewire.”

The processor(s) 610 may be, or may include, one or more programmablegeneral purpose or special-purpose microprocessors, digital signalprocessors (DSPs), programmable controllers, application specificintegrated circuits (ASICs), programmable logic devices (PLDs),field-programmable gate arrays (FPGAs), trusted platform modules (TPMs),or a combination of such or similar devices, which may be collocated ordistributed across one or more data networks. The processor(s) 610 maybe configured to execute computer program instructions from the memory620 to perform some or all of the operations for one or more of theembodiments disclosed herein. For example, the processor(s) 610 may beconfigured to execute computer program instructions from the memory 620to perform the analytic module 540 of FIGS. 5A and 5B.

The analytic system 230 may also include one or more communicationadapters 640 that may communicate with other communication devicesand/or one or more networks, including any conventional, public and/orprivate, real and/or virtual, wired and/or wireless network, includingthe Internet. The communication adapters 640 may include a communicationinterface and may be used to transfer information in the form of signalsbetween the analytic system 230 and another computer system or a network(e.g., the Internet). The communication adapters 640 may include amodem, a network interface (such as an Ethernet card), a wirelessinterface, a radio interface, a communications port, a PCMCIA slot andcard, or the like. These components may be conventional components, suchas those used in many conventional computing devices, and theirfunctionality, with respect to conventional operations, is generallyknown to those skilled in the art. In some embodiments, thecommunication adapters 640 may be used to transmit and/or receive dataassociated with the embodiments for creating the mesh generationdescribed herein.

The analytic system 230 may further include memory 620 which may containprogram code 670 configured to execute operations associated with theembodiments described herein. The memory 620 may include removableand/or fixed non-volatile memory devices (such as, but not limited to, ahard disk drive, flash memory, and/or like devices that may storecomputer program instructions and data on computer-readable media),volatile memory devices (such as, but not limited to, random accessmemory), as well as virtual storage (such as, but not limited to, a RAMdisk). The memory 620 may also include systems and/or devices used forstorage of the analytic system 230.

The analytic system 230 may also include one or more input device(s)such as, but not limited to, a mouse, keyboard, camera, and/or amicrophone connected to an input/output circuit 680. The input device(s)may be accessible to the one or more processors 610 via the systeminterface 630 and may be operated by the program code 670 resident inthe memory 620.

The analytic system 230 may also include a storage repository 650. Thestorage repository 650 may be accessible to the processor(s) 610 via thesystem interface 630 and may additionally store information associatedwith the analytic system 230. For example, in some embodiments, thestorage repository 650 may contain fluid sample data and/or analyticsdata data as described herein. Though illustrated as separate elements,it will be understood that the storage repository 650 and the memory 620may be collocated. That is to say that the memory 620 may be formed frompart of the storage repository 650.

As illustrated in FIGS. 5A and 5B, the analytic system 230 may executean analytic module 540 to analyze samples such as those communicated tothe analytic system 230 by the physical sensor array 225. The analyticmodule 540 may be capable of a number of analytic operations, includinginferring the internal state of the yeast from the data extracted fromthe fermentation process of the fermentation container 210, comparingthe progress of a given fermentation in real time to a previouslyestablished baseline, providing real time estimates of key fermentationprocess output values such as overall process time, final gravity, andfinishing pH, providing warnings when measured fermentation parametersexceed acceptable bounds, and suggesting appropriate steps that can betaken to modulate the course of a fermentation which is not proceedingproperly.

The analytic module 540 may include software and/or hardware capable ofanalyzing the data received from the physical sensor array 225 in useand generating data analysis that includes visualization outputs andend-user notification capabilities.

Unlike conventional systems, embodiments as described herein have theability to monitor, in near real-time, multiple parameters, at once,selected for their relevance and utility in subsequent analysis,automatically store the data in a purpose built database, and applyalgorithms and inference tools from computational molecular biology andinformation about regulatory networks in a fermentation organism toanalyze the course of the fermentation. In some embodiments, afermentation organism may be a fungus. In some embodiments, afermentation organism may be a yeast. Example types of yeast that may beanalyzed include, but are not limited to, Saccharomyces cerevisiae,Saccharomyces pastorianus, Saccharomyces bayanus, BrettanomycesBruxellensis, Brettanomyces Lambicus, Kluyveromyces lactis, Yarrowialipolytica, or any combination thereof.

During the fermentation process, the cells of a fermentation organisminteract with the environment to sense the conditions, extractnutrients, and metabolize or store these nutrients. As they metabolizenutrients, the cells both deplete their environment of certain moleculeswhile adding other components to it, such as protons or ethanol. Thetiming of and the rate and level at which these chemical and biochemicalchanges in the environment take place are parameters that can beobserved and which can be taken as proxies for the gene expressionprograms (regulatory networks) that are activated by the cells of thefermentation organism in response to the environmental conditions thatthe organism is experiencing.

The timing, level and rate of activation and repression of specificgenes can be indicative of the health and performance of a fermentationorganism during fermentation. The genes associated with growth andmetabolism during fermentation are part of gene expression programs(networks) that control the level of expression of a large portion ofthe genome of a fermentation organism during fermentation. Regulatoryproteins called transcription factors carry out control of geneexpression. The transcription factors work to activate or repressclusters of genes, thereby achieving the expression of specific genes atthe appropriate times with the appropriate rate and at the appropriatelevel.

The embodiments described herein incorporate novel gene expressionanalysis techniques, employed to understand the activity of thesetranscription factors and how they control gene expression duringfermentation. By analyzing and cataloging gene expression duringfermentation, and by combining this new information with alreadyavailable information on gene regulation in a fermentation organism(e.g., yeast), the genes that belong to specific clusters that aretemporally regulated during fermentation have been identified. Thesegenes control certain metabolic pathways so that with this backgroundinformation, the dynamics of chemical and biochemical parameters duringa fermentation can be used to infer information regarding the regulationstatus of specific genes. Furthermore, since these genes are regulatedin groups, the activity of many genes can be inferred from the analysisof sensor data. In some embodiments, real-time sensor data may be usedto provide an on-going and continuous view of the state of the activityof the fermentation organism.

As an example, maltose is a sugar commonly found in grain extracts thatcan be metabolized by yeast for energy and ethanol production during ananaerobic fermentation. However, maltose is not an optimum sugar foryeast to consume, as there are other sugars that require less energy tometabolize and are therefore preferentially utilized. Examples of theseoptimum sugars are the so-called simple sugars-glucose, sucrose, and/orgalactose. Maltose uptake and metabolism in yeast is repressed untilthese other sugars have been exhausted. In fermentation for beerproduction, the metabolism of maltose by yeast is an indication thatsimple sugars are depleted. However, to analyze the profile of the sugarcontent of fermentation substrates is not trivial. Rather than useexpensive enzymatic or chromatographic assays to test for theconcentration of various types of sugars at various points in thefermentation process, the sensor panel and analytical system in theinvention described here uses pH changes, in combination withinformation about gene regulation as noted above, to indicate the onsetof maltose consumption.

As an example, the yeast maltose transporter is a maltose/protonsymporter; this means that for every maltose molecule brought into ayeast cell, a proton is also carried into the cell leading to a changein pH of the fermentation substrate that can be detected by chemicalsensors. The maltose import machinery does not work in isolation. Whilethis symporter is in operation, other transporters in the cell areactively acidifying the substrate. Therefore, a change in the rate ofacidification occurs when the maltose/proton symporter is functioningamidst other biological processes. Once this change has beencharacterized, the observation of the rate of pH change in the substrateas read by the sensor panel can be used as an indicator that specificmaltose transporters are active and that the cells have shifted fromusing simple sugars and are activating genes to perform less efficientsugar metabolism while turning off genes used in glucose, galactose, andsucrose metabolism. Thus, the relationship between gene expression,sugar metabolism and pH is first characterized, and then pH and otherparameters monitored by the embodiments described herein can be used todetect the stages in the fermentation process. Further, the rates ofchange of each parameter may be analyzed and compared to discoverrelationships between parameter values and the rate of change of otherparameter values, and the relationships between the rates of change ofdifferent parameter values.

The information established from the sensors (e.g., the physical sensorarray 225 of FIG. 1 ) allows for monitoring of the physicalcharacteristics/parameters of, for example, beer during fermentation,but it also enables monitoring of fermentation performance by thefermentation organism. The metabolic processes within the fermentationorganism are responsible for the changes in chemistry of thefermentation product (e.g., beer) throughout each fermentation run.Fermentation organisms are continually responding to and remodelingtheir environment during a fermentation. Underlying the metabolicactivity of a fermentation organism is a sophisticated control systemthat senses the environment, interprets the signals, and respondsaccordingly. This control system is a gene regulatory network comprisedof proteins that activate and repress expression in response to theenvironmental signals and biological needs of the cell of thefermentation organism at that point in time. These networks responddynamically to the environment during a fermentation and the outputs aredependent upon the integration of many signaling and surveillancesystems in the cell that must be processed in context with the otherconditions. Properly analyzed and understood, the physical (e.g.,biological, chemical, etc.) parameters of each fermentation provideinsight into the operation of these networks.

Regulatory networks have been extensively mapped in S. cerevisiae.However to understand which regulatory pathways and interactions areoperational at any given time, experimental data from metabolicallyactive yeast must be observed and the transcriptional state measured. Inat least some embodiments, measurement of the transcriptional state, ormeasuring the transcriptome, includes assessing the levels of RNA foreach gene in the fermenting organism population. This can beaccomplished, for example, by sampling the fermentation, isolating theRNA from the substrate, and then purifying the RNA. The RNA can then bemeasured by fluorescence microarray hybridization, or more commonly,RNA-Sequencing. Collected data is computationally normalized and a valuefor RNA quantity corresponding to each gene in the fermenting organismis assigned. It is contemplated that this assay is performed for everytime point in the fermentation to be analyzed.

To directly detect the interaction of a network with all gene targets ispossible, but time and labor intensive. Instead, embodiments asdescribed herein employ quantitative techniques to infer these networkswith great accuracy from single time course studies. With these tools,the expression levels of genes may be connected with the properregulatory networks (RNs) and associated with the biochemistry offermentation. After mapping these RNs during fermentation, physicalparameters collected during a fermentation provide accurate indicationsof the genes enacting the biochemical pathways and their upstreamregulators. The fermentation RN identifies the brewing specificregulatory pathways up to the environmentally responsive sensorssensitive to the fermentation environment. Mapping input, to controlsystem, to output, information from the device allows the user tounderstand what conditions should be adjusted to give a desired result.Therefore, directed, specific control of the conditions within afermentation can change the fermentation performance in a predictableand reproducible manner.

FIG. 7 illustrates monitoring performance of a fermentation organism infermentation systems, according to various embodiments as describedherein. As illustrated in FIG. 7 , the operations may begin with block1000, for building a regulatory network for the fermentation organism.

FIG. 8 illustrates forming a database of regulatory networks, accordingto various embodiments as described herein (block 1000). Standard GeneExpression data from microarray or RNA-sequencing platforms, along withsensor data from fermentation may be handled in using the methodillustrated in FIG. 8 . Such a process may utilize a computationalcluster with parallelizable compute cores and local storage for pipelineanalysis results, and may utilize next generation sequencing and/ormicroarray data collected from a fermentation run during which physicalparameters were observed by the sensor panel. Gene expression data maybe summarized and normalized using transcriptomics standard operatingprocedures and quality control measures. As illustrated in FIG. 8 ,forming a database of regulatory networks for a fermentation organismmay start with block 1010 to rescale dynamics for gene expression andchemical values. In this operation data may be rescaled from 0-100 foreach dynamic value (chemical parameter measurement or gene expression).Data may be filtered so that only dynamic parameters and gene expressionvalues are considered for analysis. Using public databases, and aproprietary database of transcription factors, the dynamic geneexpression values that correspond to the transcription factors may beidentified and marked as possible regulators (block 1020). Using a suiteof modeling software, the gene expression of all targets, both geneexpression and chemical values may be compared to the gene expression ofidentified regulators (block 1030). All possible regulator-targetrelationships may be scored (block 1040) and then ranked by their scores(block 1050). The output may be a regulatory network includingtranscription factors capable of regulating the dynamics in geneexpression and chemical parameters throughout a fermentation (block1060). These models allow for regulatory and co-regulatory relationshipsbetween genes and chemical parameters to be identified.

In block 1020, regulators may be assigned to data. Transcription factorsmay be identified in this step. The genomic annotation for generegulators (activators and repressors of gene expression) may beidentified through previous experiments, gene domain discovery, andevolutionary orthology.

In block 1030, the operations may apply a regulation function for eachpossible regulator: target relationship and/or parameter value. Localconnections between regulators and downstream gene targets and chemicalchange targets may be assessed. These connections are not determined bycurve matching or autocorrelation where only the shapes of the curvesare considered and the most similar curves are matched. Instead, thecurve of the regulator may be translated by a function usingbiologically plausible parameters to test whether it can generate theoutput, in this case a target gene or chemical change. The frameworkconsiders the function f(R)=T where R is a regulator with the ability torepress or activate T by the function f The function f may containmultiple parameters derived from real world values to explain thekinetics of regulatory relationships in a biological system. For eachtarget T, every potential regulator R may be tested and a likelihoodmodel is used to obtain an optimum set of parameters for each particularpotential relationship.

In block 1040, the operations may assign a score for the fit of eachregulator: target relationship. For each target T, each potential f(R)=Tmay then be compared to find the best regulatory relationship thatexplains the target T. For this, models that best fit the real data maybe fed into the pipeline, and models that demonstrate the robustnessinherent in biological systems may be favored in a probabilitydistribution output of N regulators for each target, where N is twicethe number of R when each regulator is tested as an activator andrepressor of T.

In block 1050, the operations may rank regulator: target relationshipsby score, and may assign a confidence to each relationship in a rankedlist. Based on density of data and user input, confidence thresholds maybe put in place for the strength of local interactions computed in block1020 and 1030. From the local interactions that satisfy this cutoff,larger networks representing the underlying biology of the interactionmay be constructed.

In block 1060, the operations may build regulatory networks based onconfidence cutoffs formulated in block 1040. The gene expression networkmay be graphically represented along with the chemical value plots foreach fermentation. The coincidence of gene expression and parameterchanges as measured by the physical sensor array 225 (e.g., FIG. 1 ), aswell as the regulatory relationships between genes, can be viewed inthis way.

An example of building a gene regulatory network using a method known asa Local Edge Machine (LEM) will now be discussed. The LEM process isprovided only as an example, and it will be understood that othermethods, including various types of statistical analysis associated withthe underlying genes being tracked, will be apparent to those ofordinary skill in the art without deviating from the embodimentsdescribed herein.

Given a set of genes deemed to be potentially important for networkfunction, LEM takes a Bayesian approach to answer the followingquestion: of all possible regulators, which regulator and regulatorylogic (activation or repression) best models the expression dynamics ofeach gene? The LEM algorithm may mode the gene expression of each nodeand may score each possible regulation in the network.

Consider a gene regulatory network with a set of N nodes, ={X1,...,XN}.For i=1,...,N,X_(i)(t) denotes the expression level of gene X_(i) attime t. The data, denoted by D, consist of the observed expressionlevels of the N nodes at T time points, {t_(j)}Tj=1.

According to one model, the data are generated according to a system ofordinary differential equations (ODEs), possibly observed with noise.More specifically, for the target X_(i), a model is that X_(i),satisfies

$\begin{matrix}{\frac{\text{dX}_{i}}{\text{d}t} = \alpha_{i}f_{i}\left( {\text{X}(t)} \right) - \beta_{i}X_{i}(t) + \gamma_{i}} & \text{­­­(1)}\end{matrix}$

where X(t)=(X₁(t),...,X_(N)(t)), the function ƒ_(i): ℝ^(n)→ℝ governs thetype of regulation that X, experiences, a_(i) > 0 represents thestrength of the regulation, β_(i) ≥ 0 represents the rate of degradationof X_(i), and y_(i) ≥ 0 represents the basal rate of production ofX_(i). In general, stochastic effects may play a significant role in thedynamics of any individual cell, and such considerations lead one tostochastic differential equations. However, the data may be generated byaveraging expression levels over many (~10⁸) individual cells, and one,therefore, may assume that the stochastic effects are insignificant,leading to the use of ODEs.

Hill function kinetics may be used to model activation and repression ofthe target node. Equations of this type are not intended to model eachindividual aspect of regulation explicitly. Rather, they are intended tosubsume multiple levels of regulation (e.g., translation, transcription,chromatin modification, direct binding, etc.) into a single equationwith relatively few parameters. In general, one expects biologicalnetworks to be sparse, and even in cases where this assumption isbroken, the method may seek to identify the most dominant components ofa regulation in a given experimental condition. Thus, regulatoryfunctions ƒ_(i) of the following forms may be considered, whichcorrespond to regulation by a single gene:

$\begin{matrix}{f_{i}\left( \text{X} \right) = \left\{ \begin{array}{l}{\frac{X_{j}^{n_{i}}}{K_{i}^{n_{i}} + X_{j}^{n_{i}}}\left( {\text{activation}\mspace{6mu}\text{by}\mspace{6mu} X_{j}} \right),} \\{\frac{K_{i}^{n_{i}}}{K_{i}^{n_{i}} + X_{j}^{n_{i}}}\left( {\text{repression}\mspace{6mu}\text{by}\mspace{6mu} X_{j}} \right).}\end{array} \right)} & \text{­­­(2)}\end{matrix}$

More complex regulatory functions ƒ_(i) could be allowed in the modelclass if the goal is to infer simultaneous regulation by multiple genes.However, attention may be restricted to single regulation, since theinformation content of time-series datasets at present appears not tosupport the substantial increase in complexity of the model class thatwould result from inclusion of combinatorial regulation.

Thus, to specify a system of ODEs completely, as in Equation 1, includedherein, for each node X_(i), one may select a regulator X_(j), a type ofregulation (activation or repression), and a vector of real-valuedparameters (a_(i),n_(i),K_(i),β_(i),γ_(i)). Triples of the form(X_(i),X_(j),a) or (X_(i),X_(j),r) may be referred to as edges, where(X_(i),X_(j),a) may be interpreted as the relationship that X_(i) isactivated by X_(j) and (X_(i),X_(j),r) may denote that X_(i) isrepressed by X_(j). Note that these edges are both signed and directed.

The LEM inference method first involves making a local approximation,which allows one to infer the regulation of each node separately, ratherthan all at once. To infer the regulation of the target X (here thesubscript i is dropped from the above notation without introducingambiguity), LEM takes a Bayesian approach that utilized the Gibbsposterior principle and a Laplacian approximation in the computation ofthe posterior distribution.

In general, if M is a model (among several) and D is a dataset, thenBayes’ rule yields a posterior probability of M given the data D:

$p\left( {(M|D} \right) = \frac{p\left( {(D|M} \right)\pi(M)}{p(D)} \propto p\left( {(D|M} \right)\pi(M).$

Here p(D|M) is the likelihood of the data D given the model M, π is aprobability distribution on the possible models, called the priordistribution, and p(D) is the likelihood of D (averaged over all thepossible models). If one interprets the prior distribution as a beliefin the veracity of each model prior to generation of the data, then theposterior distribution represents the optimal way to update the beliefin light of the data. If M requires an additional choice of parameter θto be a fully generative model, the posterior distribution may bewritten as an integral over θ:

p((M|D) ∝ ∫p(D|M), θ)π(M, θ).

For LEM, the edge inference problem may be formulated in a similarmanner. Let X be a fixed node and E an edge with X as the target [i.e.,E=(X,Y,a) or E=(X,Y,r) for some node Y]. One may view E as a model forexplaining the behavior of X and employ the Bayesian framework above tocompute its posterior probability. To do so, a prior distribution on theset of possible models may be specified, which in one case is the set ofpossible edges with X as the target, and a likelihood function may beused. Recall that in this model, each edge utilizes an additional choiceof parameter vector θ=(a,β,γ,n,K) (as in Equations. 1 and 2) in order tospecify fully the corresponding differential equation.

The prior distribution may be set by the user, and there are manyopportunities for integrating other data types in this manner. However,according to embodiments as described herein, the prior distribution maybe set as follows. First, let π(E) be the uniform distribution over thepossible edges that have X as a target. For each edge E with X as thetarget, select a priori bounds on each of the parameters in θ_(E),resulting in a region R_(E) (contained in ℝ⁵) of biologically reasonableparameter values. Once these bounds are selected, one may choose themaximum entropy prior distribution subject to these bounds, which is theleast informative prior on R_(E) and ensures that the result is notunnecessarily biased. This distribution is

$\pi\left( {E,\theta} \right) = \frac{1}{s \cdot \text{Vol}\left( R_{E} \right)},$

where s is the number of edges with X as target and Vol(R_(E)) is thevolume of R_(E).

With the prior distribution set, attention may be turned to thelikelihood. In fact, as different experimental protocols could lead tosignificantly different noise models, each of which is likely to bedifficult to determine accurately and precisely, one may proceed underthe assumption that one does not have access to a likelihood function.In such cases, the Gibbs posterior principle states that the optimalmethod for updating one’s beliefs in light of the data is to replace thelikelihood p(D|M,θ) by

exp (−𝓁(D, E, θ)),

where ℓ(D,E,θ) is an appropriately chosen loss function. A loss functionℓ(D,E,θ) may be specified as follows. For a triple (D,E,θ), define thefunction F : [t₁,t_(T)] ℝ on the points

{t_(j)}_(j)^(T) = 1

by

F(t_(j)) = αf(X(t_(j))) − βX(t_(j)) + γ,

and then extend F to the whole interval [t₁,t_(T)] by linearlyinterpolating between these values. That is, if t=ut_(j)+(1-u)t_(j+1)for some j<T and u ∈ (0,1), then let F(t)=uF(t_(j)) + (1-u)F(t_(j+1)).Now set

X̂(t) = ∫_(t₁)^(t)F(s)ds,

and define the loss ℓ(D,E,θ) to be the mean squared error between theobserved values

{X(t_(j))}_(j = 1)^(T)

and the properly shifted model prediction

{X̂(t_(j))}_(j = 1)^(T):

$\mathcal{l}\left( {D,E,\theta} \right) =_{c \in {\mathbb{R}}}^{min}\frac{1}{T}{\sum\limits_{j = 1}^{T}\left( {X\left( t_{j} \right) - \hat{X}\left( t_{j} \right) - c} \right)^{2}}.$

This choice of loss function is effectively equivalent to the choice ofa Gaussian noise model.

With the prior distribution and the loss function now specified, the(marginal) Gibbs posterior probability of the edge E given the data is

$p\left( {(E|D} \right) \propto {\int_{R_{E}}{\exp\left( {- \mathcal{l}\left( {D,E,\theta} \right)} \right)\frac{d\theta}{s\mspace{6mu}\text{Vol}\left( R_{E} \right)}}} \cdot (3)$

As is common in many Bayesian methods, the above integral does not havea closed-form solution. A Laplace approximation may be chosen toestimate it. From this approximation, one can see that LEM explicitlyfavors networks whose dynamics are more robust to a perturbation in theparameter space. In principle, one could attempt to compute otherapproximations of this integral, including Monte Carlo approximations.However, the Laplace approximation has been found to be computationallyfast and produces sufficiently accurate results for the purposes of theembodiments described herein.

Thus, the output of LEM may be N different probability distributions-onefor each node in the network. The distribution for node X may beinterpreted as representing which edge is the dominant regulatoryinteraction (edge) controlling the expression of X. There are multipleways to obtain a single network from this set of distributions, thesimplest of which is to select the most likely edge from eachdistribution.

Referring again to FIG. 7 , once the regulatory network has been formed,monitoring performance of a fermentation organism in fermentationsystems may continue with operation 2000 to establish baselines.

For some fermentation systems, such as beer, the end user has targetvalues in mind for many of the physical parameters (e.g., chemical,biological, etc.) of the substrate. Continuous monitoring of theseparameters, such as pH, density/gravity (as an indirect measure ofethanol), and dissolved oxygen may allow an end user to detect when thefermentation reaches particular milestones. The rate at which thesetargets are reached is an indicator of the health and performance of afermentation organism. As these values are monitored continuously, therate and values are both determined and used for analysis. Thefermentation substrate is complex and often specific to a particularfermentation process or fermentation product (e.g., a brewery, beer).

Therefore, best practices with the sensor panel and associatedanalytical tools may include establishing acceptable baselinefermentations (and their associated parameters) by monitoring a seriesof fermentations and identifying the acceptable and unacceptablefermentation performances. The baseline performance threshold may be setby the ensemble performance of the acceptable fermentations. Such acalibration may allow the end user and the analytical pipeline toidentify the normal parameter values, rates of change, and therelationships between parameters that occur during a normalfermentation.

The establishment of this baseline will allow intervention intofermentations that fall outside of acceptable parameters. Severalexample interventions are provided below:

Example 1: If density/gravity readings do not correlate with appropriatechanges in pH during a fermentation, it may indicate that thefermentation organism that is consuming the substrate may not beacidifying the substrate. This is due to the fact that the regulatorynetworks discovered in the fermentation organism species are constrainedby the type of outputs they can create and how the parameters mustchange. The regulatory networks regulate gene expression programs that,for example, consume the substrate but also acidify the substrate at aparticular rate. If the parameter values indicate a deviation from thetype of relationship between parameters observed under normalfermentation conditions using this fermentation organism species, thenthere may be evidence that another biological regulatory network is atplay during the fermentation. This is a strong indication of microbialcontamination with another microorganism with a different set ofregulatory network relationships and constraints. This batch should bediscarded and the fermenter vessel cleaned. In the case of a bioethanolproduction run, the substrate could be heated to destroy thecontamination and re-inoculate with yeast to initiate a correctedfermentation run.

Example 2: When initial oxygen levels (within the first hour of afermentation) are not high enough to provide a fermentation organismwith the molecular oxygen needed for proper growth, oxygen may be addedwithin the first several hours to improve proliferation and fermentationperformance of the fermentation organism.

Example 3: If pH levels do not drop, for example, within the first 24hours, this may indicate poor health of a fermentation organism. To aidthe organism with fermentation and to make conditions inhospitable forsome potential contaminants, acids may be added. Depending on thefermentation, organic acids such as lactic acid or inorganic acids suchas phosphoric acid, hydrochloric acid, or sulfuric acid can be added tothe fermentation substrate for acidification. Fermentation performanceby yeast, for example, is optimal in low pH ranges (approximately3.5-4.5).

Example 4: Increases in pH and dissolved oxygen levels at a late stagein a fermentation process could indicate a die off of yeast due toalcohol intolerance. In some embodiments, a late stage of thefermentation process may be a stage after the bulk of sugars have beenconsumed, and/or the density is within approximately 15% of thefinishing parameter values. Certain vitamins and antioxidant compoundscan be added to the substrate at this point. These vitamins may bereadily taken up by a fermentation organism to help mitigate further dieoff and promote fermentation performance. The addition of vitamins andantioxidant

compounds at earlier points in the process may not be effective, as theyare simply metabolized.

A database, such as the data storage 650 of the analytic system 230 (seeFIGS. 1, 5A, 5B, and 6 ) may store the data and can be used for analysisof:

the particular fermentation in question compared to other fermentationsof the same product, by the same company (or in the same facility);

the particular fermentation in question compared to fermentations ofsimilar products by other fermentation companies or at other sitesproducing the same product;

the particular fermentation in question compared to other fermentationsof other products produced by the same company;

the particular fermentation in question compared to other fermentationsof other products by others; and other similar comparisons betweenproducts produced at the same or different facilities or by differentcompanies.

The database, in turn, may provide deeper insight into the performanceand fermentation by a fermentation organism, using customized algorithmscapable of discovering relationships among the parameters involved.

In some embodiments, baselines for a particular end user may be adoptedfrom another set of fermentation processes. That is to say, themonitoring system may not necessarily require that baselines beestablished for specific equipment before performance monitoring can beaccomplished. In some embodiments, baselines from similar equipment,similar brewing practices, and/or similar brewing procedures may beadopted by the monitoring system.

Referring again to FIG. 7 , once one or more baselines have beenestablished, monitoring in fermentation systems may continue withoperation 3000 to perform fermentation and performance control onsubsequent fermentations.

FIG. 9 illustrates fermentation and performance control, according tovarious embodiments as described herein (FIG. 7 , block 3000). Asillustrated in FIG. 9 , methods, systems, and computer program productscan include receiving the measurement of the at least one physicalparameter of the material sampled from the fermentation tank (block3010), comparing the measurement of the at least one physical parameter(e.g., chemical, biological, etc.) of the material sampled from thefermentation tank to a baseline value of the at least one physicalparameter for the fermentation process (block 3020), comparing thephysical parameter to a baseline value (block 3030), and responsive to adeviation of the measurement of the at least one physical parameter ofthe material sampled from the fermentation tank from the baseline value,determining a remediation action based on a correlation between the atleast one physical parameter and one or more regulatory genes of thefermentation organism (block 3040). FIG. 10 illustrates an example ofthe process illustrated in FIG. 9 .

As illustrated in FIG. 10 , the sensor data, as interpreted by thepipeline, may aid the operator in making decision on process controlthat are rooted in understanding the biological interplay between yeastand the fermentation substrate. The flow chart of FIG. 10 describespotential interventions based upon the results output from theinvention.

The sensor data (e.g., physical parameters of the fermentationsubstrate) may be interpreted by the pipeline and compared against theestablished baseline. The dynamics of the physical parameters arecompared and an estimated time of completion for the fermentation isprovided within the first 24 hours of fermentation.

The sensor data may be continuously tested for stationarity during afermentation. When dynamic changes in physical parameters are completeand the yeast is no longer modifying the environment and convertingcarbon sources to ethanol. In such a case, the operator may be alertedthat the fermentation is complete.

In the event that the fermentation does not perform to specification,the sensor data as interpreted by the platform may provide informationthat the operator may use to make decisions on interventions and processcontrol decisions. Out of parameter alerts may be provided when suchevents occur. The flow chart of FIG. 10 describes suggested examplecourses of action dependent on the alert.

For example, if pH alert occurs at the onset of fermentation, a foodgrade acid or base can be added to the fermentation vessel to adjust thepH back to specification. This may allow for the regulatory network thatexists within the fermenting organisms to receive the proper signalingthat the conditions are optimized for the particular fermentation. Theorganisms’ regulatory networks then signal to the proper outputs so thatthe fermentation performance is maximized. The amount to be added andthe desired pH level may differ based on time of fermentation. The typeof acid or base depends on the product being made.

For example, if the dissolved oxygen (DO) is out of specification, thecourse of action may also be dependent upon the timing of fermentation.

If the DO is too low, then, if the fermentation is within the firstabout 6 hours, this is the interval during which added oxygen may aidthe fermentation and it should be added to the fermentation.

If the fermentation is after the first about 6 hours, oxygen can have adetrimental effect on ethanol production and should not be added. Theoperator should expect a longer fermentation time. In some embodiments,if available, the operator may choose to add fresh yeast that has beenoxygenated prior to introduction into fermentation vessel.

If the DO is too high, the operator should take note and adjust theprocess SOP (standard operating procedure) to reduce oxygen supply tothe fermentation for subsequent fermentations to save on cost andmaterials.

For example, the relationship between density of the fermentationsubstrate and pH may indicate the performance of the fermentationorganism, e.g., yeast, in the conversion of the substrate carbohydratesources to a product, e.g., ethanol.

If the relationship between pH and density falls outside the parameterspecification determined by the baseline, microbiology techniques may beused to determine if contaminating organisms are present in thefermentation. If contamination levels are deemed too high, fermentationmay be stopped and the equipment cleaned.

If pH can be corrected, the operator may consider adding food gradeacids or base to bring pH back into an optimal window for fermentationperformance.

If minimal contamination exists, or if the concentration of viable,vital cells of a fermentation organism is too low, the operator canconsider outcompeting other microbes by adding further of thefermentation organism, e.g., fresh yeast.

If addition of yeast is not possible, the operation may consideraddition of yeast extract to increase available nitrogen for yeast.

For example, conductivity provides an indication of the ions present inthe fermentation substrate. If the fermentation substrate shows aninitial conductivity reading that is lower than targeted as per thespecification, the operator could check with the operator’s watersupplier to determine if water chemistry profile has changed. Theoperator could also consider adding specific salts that contribute tothe typical water profile used for the specific product/facility, suchas calcium salts, magnesium salts, and/or sodium salts. The operatorcould also consider adding electrolytes that contribute to the health ofthe yeast to achieve higher fermentation performance, such as zincsalts, manganese salts, and/or copper salts.

If the conductivity reading suggests an ion concentration that is toohigh, the operator could check with water supplier to determine if waterchemistry profile has changed.

The operator could also consider reverse osmosis filtration or othermeans of removing ions from the fermentation substrate water.

For example, temperature may affect the fermentation performance of afermentation organism. The optimum temperature of a fermentationsubstrate is highly dependent on the particular fermentation organism orthe strain or species of fermentation organism, or desired sensorycompound output. Thermostat controlled glycol (or other coolant)temperature control systems typically control fermentation substratetemperature. However, temperature variation within a fermentation canhave a negative effect on fermentation performance by a fermentationorganism. If large variations in fermentation temperature occur, theoperator could consider adjustment upper and lower bounds on thermostat.For fermentations that demand tighter temperature control, the operatorcould consider utilization of other fermenters within facility withimproved temperature control. The operator could also consider changesin heating/cooling system to better match the needs of the facility

As described herein, the analytic system 230 may make continuous samplesof data from the fluidic sampling apparatus 220. The data may representsamples of the physical parameters of the medium in the fermentationcontainer 210 as sampled by the physical sensor array 225. The analyticsystem 230 may utilize an analysis suite, such as the analytic module540 (see FIGS. 5A and 5B), to analyze the samples.

The analysis suite may be built upon a custom database that acquires andstores the real-time data as it is uploaded to off-site servers (atintervals from 1 second to 10 minutes depending on network bandwidth andconnectivity).

The purpose of real-time monitoring is to ensure that the fermentationproceeds according to expectations and previously established baselines.The brewer or fermentation supervisor can also access the informationelectronically using a Graphical User Interface (GUI). From this GUI,the end user will be able to monitor data from the fermentation in nearreal-time (as it is uploaded). Prior to each fermentation, the end userhas the opportunity to add parameters for each of the sensor outputs. Ifthe readings from the sensors on a fermenter fall outside a setparameter range, the unit will alarm and send an alert SMS, email, orother notification to the client of the out-of-parameter value. This isespecially useful for facilities that are not manned 24 hours a day or 7days a week.

The information from each monitored fermentation may be stored andorganized in the database by customer, product type or style (in beerand beverage industry), starting nutrient levels, ethanol targets,fermentation duration targets, and/or other metadata that the clientdesires to use to organize their fermentations within their account.Customer information may be securely protected.

In some embodiments, the present invention provides a method forconstructing a baseline database for a selected fermentation process bya fermentation organism in a fermentation substrate, the methodcomprising: (a) measuring a physical parameter at multiple time pointsduring the selected fermentation process from initiation to terminationof the selected fermentation process, thereby providing values (or arate of change) for the physical parameter over time for the selectedfermentation process; (b) measuring a transcriptome of the fermentationorganism at the same time points during the fermentation process asmeasured for the physical parameter to produce a gene expressiondatabase over time for the selected fermentation process; (c) inferringregulatory networks of the fermentation organism from the geneexpression database; and (d) identifying one or more regulatory genes ofthe fermentation organism that are correlated with a value or range ofvalues (or a rate of change) for the physical parameter measured for theselected fermentation process, thereby constructing the baselinedatabase for the selected fermentation process that provides apredetermined value or range of values (or predetermined rate of change)for the parameter that is correlated with the one or more regulatorygenes of the fermentation organism.

Also provided is a baseline database constructed by the methods of theinvention.

In some embodiments, a method of standardizing a selected fermentationprocess by a fermentation organism in a fermentation substrate isprovided, comprising: (a) measuring a physical parameter at multipletime points during the selected fermentation process from initiation totermination of the selected fermentation process, thereby providingvalues (or a rate of change) for the physical parameter over time forthe selected fermentation process; and (b) comparing values (or a rateof change) of the physical parameter measured for the selectedfermentation with predetermined values (or predetermined rate of change)for the same physical parameter provided by the baseline database, (c)modifying a fermentation condition to increase or decrease theexpression of one or more regulatory genes (i.e., decrease or increaserepression or activation of the one or more regulatory genes) of thefermentation organism identified in the baseline database as correlatedwith the physical parameter when the values (or the rate of change) ofthe physical parameter measured for the selected fermentation processfall outside the predetermined range of values (or the predeterminedrate of change) for the same physical parameter, therebymodifying/adjusting the values (or the rate of change) for the physicalparameter so that they fall within the predetermined range of values (orthe predetermined rate of change) of the baseline database andstandardizing the selected fermentation process.

In some embodiments, measuring a physical parameter at multiple timepoints comprises measuring the parameter at least every 15 seconds toevery five minutes from initiation to termination of the selectedfermentation process.

In some embodiments, the physical parameter that is measured mayinclude, but is not limited to, dissolved oxygen level, ethanol level,pH, CO₂ level, density, gravity, cell concentration and/or electricalconductivity.

In some embodiments, the fermentation organism may be any organism thatis capable of fermentation, including, but not limited to, a fungus, abacteria, or an algae (e.g., microalgae). In some embodiments, thefermentation organism may be any yeast or any combination of differentyeast species or different yeast strains. In some embodiments, the yeastmay be Saccharomyces cerevisiae, Saccharomyces pastorianus,Saccharomyces bayanus, Brettanomyces Bruxellensis, BrettanomycesLambicus, Kluyveromyces lactis, Yarrowia lipolytica, or any combinationthereof.

In some embodiments, the one or more regulatory genes may be atranscription factor. Any transcription factor known or later identifiedto be present in a fermentation organism may be used. In someembodiments, a transcription factor useful with this invention mayinclude, but is not limited to, OAF 1, PDR3, HIR1, HAP3, RTG3, REB1,NRG2, TEC1, SMP 1, HPC2, THI2, MAL33, KAR4, HCM1, RDS1, RPN4, MBP1,PHO2, UGA3, LYS14, NRG1, PDC2, GIS1, INO2, SWI5, UME6, UPC2, ADR1,MET32, YAP6, MTH1, SUM1, ARO80, CAD1, YHP1, STP1, GCN4, MIG3, GLN3,ACA1, DOT6, FLO8, SWI4, SPT2, RPH1, GAT1, HAC1, CDC14, PHO4, PDR1, MIG1,AFT1, HSF1, TOS8, SUT1, CUP2, GTS1, IME4, MIG2, HAP2, RTG2, FZF1, RME1,MGA1, MAL13, YAP3, OPI1, RIM101, STP2, RSC30, STE12, NDT80, STB5, RPN10,SKN7, CST6, XBP1, FKH1, IMP2, GAT4, MET28, YAP5, DAL81, MGA2, ZAP1,SIP4, GZF3, CBF1, IME1, RSF2, HMS2, BYEI, PUT3, SPT23, IXR1, RGTI, PHD1,MSN4, HAP4, ABF1, ASHI, DAL80, BASI, GAT3, PPR1, CHA4, ACE2, RFX1, SW16,IFH1, ECM22, HAPI, PDR8, STP3, SFP1, LEU3, YAPI, YOX1, GAL80, WARI,ARG81, SOK2, MACI, MSN2, ARG80, MCM1, MOT3, MSS11, HOT1, RGM1, CAT8,ELP6, CRZ1, FKH2, MET4, SKO1, GCR2, SPS18, RAP1, GIS2, DAL82, YAP7,RTG1, HAL9, INO4, MSN1, CIN5, HMS1, HIR2, AZFI, SFL1, YRR1, YRMI, TYE7,HAP5, PIP2, NDD1, RDR1, MET31, GCR1, RLM1, RDS2, UME1, CUP9, AFT2, GAL4,MDL2, HAA1, YPRO15C, ROX1, RDS3, FHL1, ARR1, or any homologue thereof(see, e.g., YEASTRACT database; Teixeira et al. Nucl. Acids Res., 42(D1)D161-D166 (2014)).

In some embodiments, a physical parameter that may be measured can bethe density of the fermentation substrate and the one or more genes thatcorrelate with density may include, but are not limited to, ADH1, ADH3,ADH4, ADH5, PDC1, PYK1, ENO1, PGM1, PGK1, TDH1, any homologue thereof,or combination thereof. If the density physical parameter is too low,additional fermentable sugars may be added to the substrate. If thedensity physical parameter is too high, additional water and yeast maybe used to dilute out the fermentable sugar and return density to thedesired value.

In some embodiments, a physical parameter that may be measured can bethe pH of the fermentation substrate and the one or more genes thatcorrelate with pH may include, but are not limited to, PMA1, CAN1,PDR12, ALP1, any homologue thereof, or combination thereof If the pH istoo high, food grade acids may be added to the fermentable substrateuntil the target pH is achieved. If the pH is too low, food grade basicchemicals may be added to the fermentable substrate until the target pHis achieved.

In some embodiments, a physical parameter that may be measured can bethe conductivity of the fermentation substrate and the one or more genesthat correlate with conductivity may include, but are not limited to,PMA1, ENA1, NHA1, TRK1, TRK2, TOK1, PMR1, PMC1, CCH1, MIDI, ZRT1, ZRT2,any homologue thereof, or combination thereof. If the conductivity ofthe fermentable substrate is too high, additional water, yeast, andfermentable sugar may be added to reduce the electrolyte concentrationof the solution. If the conductivity of the fermentable substrate is toolow, additional food grade electrolytes could be added so as to increasethe substrate conductivity.

In some embodiments, a physical parameter that may be measured can bethe dissolved oxygen in the fermentation substrate and the one or moregenes that correlated with dissolved oxygen may include, but are notlimited to, COX4, COX5, COX6, COX7, COX8,COX9, COX12, COX13, COX10,YAH1, ARH1, any homologue thereof, or combination thereof. If thedissolved oxygen levels of the substrate are too low, additional oxygencould be added to the fermentation vessel. If dissolved oxygen levelsare too high, remediation may involve encouraging the fermentingorganism to scavenge the oxygen by changing the fermentation conditionsto favor oxygen scavenging, potentially by changing the fermentationtemperature or mixing rate.

In some embodiments, a physical parameter that may be measured can bethe temperature of the fermentation substrate and the one or more genesthat correlate with temperature may include, but are not limited to,HSP104, HSP42, HSP82, CTO1, any homologue thereof, or combinationthereof. If temperature is not within an acceptable parameter, theoperator should adjust using their heat-exchange system if available.

In some embodiments, a physical parameter that may be measured can becell concentration of the fermentation organism in the fermentationsubstrate and the one or more genes that correlate with cellconcentration may include, but are not limited to CLN1, CLN2, CLN3,CLB1, CLB2, CLB3, CLB4, CLB5, CLB6, MBP1, SWI4, FKH1, FKH2, NDD1, ACE2,SWI5, HCM1, YHP1, YOX1 any homologue thereof, or combination thereof. Ifthe cell concentration is too low, more cells of the fermenting organismcan be added to increase the cell concentration. If cell concentrationis too high, additional fermentation substrate could be added to thevessel where possible.

In some embodiments, modifying or adjusting a fermentation condition maycomprise adding fresh yeast, verifying the presence of a contaminatingorganism, supplementing the fermentation substrate with a carbohydratesource or other nutrients, delaying the termination of the fermentationprocess, accelerating the termination of the fermentation process,increasing the temperature of the fermentation substrate, or decreasingthe temperature of the fermentation substrate. Example carbohydrates foraddition include, but are not limited to, glucose, lactose, galactose,maltose, maltotriose, maltotetraose, glycogen, and/or maltodextrin.Example nutrients for addition can include but are not limited todiammonium phosphate, yeast extract, vitamins, iron, zinc salts,potassium salts, magnesium salts, calcium salts, and/or sodium salts.

In some embodiments, when a contaminating organism is identified anintervention appropriate for the specific fermentation may beundertaken, up to and including terminating the fermentation). Thus, insome embodiments, when a contaminating organism is identified, thefermentation may be stopped and the fermentation system (tank and otherinstruments) is decontaminated/sterilized.

In the above-description of various embodiments, it is to be understoodthat the terminology used herein is for the purpose of describingparticular embodiments only and is not intended to be limiting of thevarious embodiments as described herein. Unless otherwise defined, allterms (including technical and scientific terms) used herein have thesame meaning as commonly understood by one of ordinary skill in the artto which this disclosure belongs. It will be further understood thatterms, such as those defined in commonly used dictionaries, should beinterpreted as having a meaning that is consistent with their meaning inthe context of this specification and the relevant art and will not beinterpreted in an idealized or overly formal sense unless expressly sodefined herein.

Like numbers refer to like elements throughout. Thus, the same orsimilar numbers may be described with reference to other drawings evenif they are neither mentioned nor described in the correspondingdrawing. Also, elements that are not denoted by reference numbers may bedescribed with reference to other drawings.

When an element is referred to as being “connected,” “coupled,”“responsive,” or variants thereof to another element, it can be directlyconnected, coupled, or responsive to the other element or interveningelements may be present. In contrast, when an element is referred to asbeing “directly connected,” “directly coupled,” “directly responsive,”or variants thereof to another element, there are no interveningelements present. Like numbers refer to like elements throughout.Furthermore, “coupled,” “connected,” “responsive,” or variants thereofas used herein may include wirelessly coupled, connected, or responsive.As used herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. Well-known functions or constructions may not be described indetail for brevity and/or clarity. The term “and/or” includes any andall combinations of one or more of the associated listed items.

As used herein, the terms “comprise,” “comprising,” “comprises,”“include,” “including,” “includes,” “have,” “has,” “having,” or variantsthereof are open-ended, and include one or more stated features,integers, elements, steps, components or functions but does not precludethe presence or addition of one or more other features, integers,elements, steps, components, functions or groups thereof.

As used herein, the transitional phrase “consisting essentially of meansthat the scope of a claim is to be interpreted to encompass thespecified materials or steps recited in the claim and those that do notmaterially affect the basic and novel characteristic(s) of the claimedinvention. Thus, the term “consisting essentially of when used in aclaim of this invention is not intended to be interpreted to beequivalent to “comprising.”

Example embodiments are described herein with reference to blockdiagrams and/or flowchart illustrations of computer-implemented methods,apparatus (systems and/or devices) and/or computer program products. Itis understood that a block of the block diagrams and/or flowchartillustrations, and combinations of blocks in the block diagrams and/orflowchart illustrations, can be implemented by computer programinstructions that are performed by one or more computer circuits. Thesecomputer program instructions may be provided to a processor circuit ofa general purpose computer circuit, special purpose computer circuit,and/or other programmable data processing circuit to produce a machine,such that the instructions, which execute via the processor of thecomputer and/or other programmable data processing apparatus, transformand control transistors, values stored in memory locations, and otherhardware components within such circuitry to implement thefunctions/acts specified in the block diagrams and/or flowchart block orblocks, and thereby create means (functionality) and/or structure forimplementing the functions/acts specified in the block diagrams and/orflowchart block(s).

These computer program instructions may also be stored in a tangiblecomputer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instructions whichimplement the functions/acts specified in the block diagrams and/orflowchart block or blocks.

A tangible, non-transitory computer-readable medium may include anelectronic, magnetic, optical, electromagnetic, or semiconductor datastorage system, apparatus, or device. More specific examples of thecomputer-readable medium would include the following: a portablecomputer diskette, a random access memory (RAM) circuit, a read-onlymemory (ROM) circuit, an erasable programmable read-only memory (EPROMor Flash memory) circuit, a portable compact disc read-only memory(CD-ROM), and a portable digital video disc read-only memory(DVD/Blu-Ray).

The computer program instructions may also be loaded onto a computerand/or other programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer and/or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions which execute on the computer or otherprogrammable apparatus provide steps for implementing the functions/actsspecified in the block diagrams and/or flowchart block or blocks.Accordingly, embodiments of the present disclosure may be embodied inhardware and/or in software (including firmware, resident software,microcode, etc.) that runs on a processor such as a digital signalprocessor, which may collectively be referred to as “circuitry,” “amodule,” or variants thereof.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousaspects of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should be noted thateach block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It should also be noted that in some alternate implementations, thefunctions/acts noted in the blocks may occur out of the order noted inthe flowcharts. For example, two blocks shown in succession may in factbe executed substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved. Moreover, the functionality of a given block of the flowchartsand/or block diagrams may be separated into multiple blocks and/or thefunctionality of two or more blocks of the flowcharts and/or blockdiagrams may be at least partially integrated. Finally, other blocks maybe added/inserted between the blocks that are illustrated. Moreover,although some of the diagrams include arrows on communication paths toshow a primary direction of communication, it is to be understood thatcommunication may occur in the opposite direction to the depictedarrows.

EXAMPLES

The system has been reduced to practice and fermentation performanceduring beer production has been monitored. The data was uploaded in realtime to a database where it was accessed. These data demonstrate some ofthe dynamic changes in the chemical and physical properties of beer orother liquid fermentation substrates during the course of a singlefermentation. See, FIGS. 11 and 12 that illustrate data fromexperimental implementations of a fermentation monitoring system,according to various embodiments as described herein. The data from eachsensor run may be visualized separately or aligned with other data typesby time (FIG. 11 ). Additionally, sensor data from more than onefermentation may be aligned and then visualized for the purposes ofmonitoring reproducibility or understanding how a change in conditionsor recipe might impact fermentation performance (FIG. 12 ). In FIG. 11 ,multiple parameters as measured by real-time sensors of a fermentationperformance during production of beer are shown. In this example,separate sensors in the same apparatus are measuring the temperature, pHand dissolve oxygen concentration during the first 40 hours offermentation. In FIG. 12 , pH levels as monitored using real-timesensors from two different fermentations using the same recipe brewed ondifferent days are shown. The data provided in FIG. 12 is frominitiation of fermentation through 16 hours post-initiation.

FIG. 13 is a heat map visualization of gene expression in S. cerevisiaeduring a beer fermentation from a transcriptomics analysis according tovarious embodiments as described herein. The ~700 genes were selectedduring analysis for their highly dynamic behavior during beerfermentation. Each line in the graph is a single gene, and the genes areplotted from beginning to end of the fermentation analysis, left toright. In the figure, each gene is normalized to its own meanexpression. When a gene is highly expressed, the block is brighterwhite. When the expression of the gene is low, the block appears darker.This demonstrates the dynamic behavior of gene expression during beerfermentation by budding yeast. This is a visual representation of thetype of data fed into the analytical pipeline of the various embodimentsas described herein.

FIG. 14 is a selection of line plots of gene expression in S. cerevisiaeduring a beer fermentation from a transcriptomics analysis according tovarious embodiments as described herein. The four genes were selectedduring analysis for their dynamic behavior and involvement infermentation relevant processes. Each line is a single gene normalizedto its own mean expression. These genes are plotted over time during abeer fermentation. These genes are under regulation during beerfermentation by budding yeast. This is a visual representation of thetype of data fed into the analytical pipeline of the various embodimentsas described herein.

Many different embodiments have been disclosed herein, in connectionwith the above description and the drawings. It will be understood thatit would be unduly repetitious and obfuscating to literally describe andillustrate every combination and subcombination of these embodiments.Accordingly, the present specification, including the drawings, shall beconstrued to constitute a complete written description of variousexample combinations and subcombinations of embodiments and of themanner and process of making and using them, and shall support claims toany such combination or subcombination. Many variations andmodifications can be made to the embodiments without substantiallydeparting from the principles of the present invention. All suchvariations and modifications are intended to be included herein withinthe scope of the present invention.

1. A method of standardizing a selected fermentation process by afermentation organism in a fermentation substrate, the methodcomprising: (A) constructing a baseline database for the selectedfermentation process by the fermentation organism in the fermentationsubstrate by: measuring one or more physical parameters for a fluidicsample at a corresponding time point, measuring, for each fluidicsample, a transcriptome of the fermentation organism at thecorresponding time point, constructing a baseline database for theselected fermentation process; and (B) initiating a second instance ofthe selected fermentation process by the fermentation organism in thefermentation substrate, comprising: obtaining a second fluidic sample ateach respective time point, measuring one or more physical parametersfor the second fluidic sample at the corresponding respective timepoint, and comparing measured physical parameter values for the secondfluidic sample to preferred values and ranges of values specified incondition sets of the baseline database.